@conference{Abdelzaher2018,
title = {Will Distributed Computing Revolutionize Peace? The Emergence of Battlefield IoT},
author = {Tarek Abdelzaher and Nora Ayanian and Tamer Basar and Suhas Diggavi and Jana Diesner and Deepak Ganesan and Ramesh Govindan and Susmit Jha and Tancrede Lepoint and Ben Marlin and Klara Nahrstedt and David Nicol and Raj Rajkumar and Stephen Russell and Sanjit Seshia and Fei Sha and Prashant Shenoy and Mani Srivastava and Gaurav Saukhatme and Ananthram Swami and Paulo Tabuada and Don Towsley and Nitin Vaidya and Venu Veeravalli},
url = {https://ieeexplore.ieee.org/document/8416375/},
year = {2018},
date = {2018-07-02},
booktitle = {Proc. IEEE International Conference on Distributed Computing Systems (ICDCS)},
address = {Vienna, Austria},
abstract = {An upcoming frontier for distributed computing might literally save lives in future military operations. In civilian scenarios, significant efficiencies were gained from interconnecting devices into networked services and applications that automate much of everyday life from smart homes to intelligent transportation. The ecosystem of such applications and services is collectively called the Internet of Things (IoT). Can similar benefits be gained in a military context by developing an IoT for the battlefield? This paper describes unique challenges in such a context as well as potential risks, mitigation strategies, and benefits.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}

An upcoming frontier for distributed computing might literally save lives in future military operations. In civilian scenarios, significant efficiencies were gained from interconnecting devices into networked services and applications that automate much of everyday life from smart homes to intelligent transportation. The ecosystem of such applications and services is collectively called the Internet of Things (IoT). Can similar benefits be gained in a military context by developing an IoT for the battlefield? This paper describes unique challenges in such a context as well as potential risks, mitigation strategies, and benefits.

Running data-parallel jobs across geo-distributed sites has emerged as a promising direction due to the growing need for geo-distributed cluster deployment. A key difference between geo-distributed and intra-cluster jobs is the heterogeneous (and often constrained) nature of compute and network resources across the sites. We propose Tetrium, a system for multi-resource allocation in geo-distributed
clusters, that jointly considers both compute and network resources for task placement and job scheduling. Tetrium significantly reduces job response time, while incorporating several other performance goals with simple control knobs. Our EC2 deployment and trace-driven simulations suggest that Tetrium improves the average job response time by up to 78% compared to existing data-locality-based solutions, and up to 55% compared to Iridium, the recently proposed geo-distributed analytics system.

Like today's autonomous vehicle prototypes, vehicles in the future will have rich sensors to map and identify objects in the environment. For example, many autonomous vehicle prototypes today come with line-of-sight depth perception sensors like 3D cameras. These cameras are used for improving vehicular safety in autonomous driving, but have fundamentally limited visibility due to occlusions, sensing range, and extreme weather and lighting conditions. To improve visibility and performance, not just for autonomous vehicles but for other Advanced Driving Assistance Systems (ADAS), we explore a capability called Augmented Vehicular Reality (AVR). AVR broadens the vehicle's visual horizon by enabling it to share visual information with other nearby vehicles, but requires careful techniques to align coordinate frames of reference, and to detect dynamic objects. Preliminary evaluations hint at the feasibility of AVR and also highlight research challenges in achieving AVR's potential to improve autonomous vehicles and ADAS.